1. Field of the Invention
The present disclosure generally relates to signal processing, and more specifically to compressive signal processing to efficiently identify geometric features of a signal.
2. Description of Background
Traditional approaches to identification of geometric features typically acquire the entire signal via uniform sampling, and then scan the entire signal to identify features. Standard compressive sensing based approaches recover the entire signal from compressive samples, and then identify features by scanning the reconstructed signal. Optical superposition or multiplexing methods superimpose different parts of a scene to enable wide field of view cameras for target detection and tracking.
The conventional approaches described above have several disadvantages. For example, uniform sampling based methods expend significant energy acquiring the entire signal, even though only small parts of the signal contain the features of interest. Standard compressive sensing based approaches require a computationally expensive algorithm to recover the signal; the recovered signal is then used as an input to feature identification. Furthermore, even the most sparse compressive sensing encoding matrices have a number of nonzeros per column that is a logarithmic function of signal length and sparsity. Such matrices may be challenging to instantiate in hardware. Optical superposition methods superimpose different parts of the image in such a way that the recovery of the target position in the original scene is ambiguous, and requires additional knowledge about the target object such as a dynamic model.